Overview

Dataset statistics

Number of variables17
Number of observations3390
Missing cells510
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory733.4 KiB
Average record size in memory221.5 B

Variable types

Numeric9
Categorical7
Boolean1

Alerts

cigsPerDay is highly overall correlated with is_smokingHigh correlation
diaBP is highly overall correlated with prevalentHyp and 1 other fieldsHigh correlation
diabetes is highly overall correlated with glucoseHigh correlation
glucose is highly overall correlated with diabetesHigh correlation
is_smoking is highly overall correlated with cigsPerDayHigh correlation
prevalentHyp is highly overall correlated with diaBP and 1 other fieldsHigh correlation
sysBP is highly overall correlated with diaBP and 1 other fieldsHigh correlation
BPMeds is highly imbalanced (80.6%)Imbalance
prevalentStroke is highly imbalanced (94.4%)Imbalance
diabetes is highly imbalanced (82.8%)Imbalance
education has 87 (2.6%) missing valuesMissing
BPMeds has 44 (1.3%) missing valuesMissing
totChol has 38 (1.1%) missing valuesMissing
glucose has 304 (9.0%) missing valuesMissing
id is uniformly distributedUniform
id has unique valuesUnique
cigsPerDay has 1703 (50.2%) zerosZeros

Reproduction

Analysis started2025-12-26 18:41:19.489977
Analysis finished2025-12-26 18:41:32.690700
Duration13.2 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Uniform  Unique 

Distinct3390
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1694.5
Minimum0
Maximum3389
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size26.6 KiB
2025-12-27T00:11:32.835141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile169.45
Q1847.25
median1694.5
Q32541.75
95-th percentile3219.55
Maximum3389
Range3389
Interquartile range (IQR)1694.5

Descriptive statistics

Standard deviation978.75303
Coefficient of variation (CV)0.5776058
Kurtosis-1.2
Mean1694.5
Median Absolute Deviation (MAD)847.5
Skewness0
Sum5744355
Variance957957.5
MonotonicityStrictly increasing
2025-12-27T00:11:33.017945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
22771
 
< 0.1%
22531
 
< 0.1%
22541
 
< 0.1%
22551
 
< 0.1%
22561
 
< 0.1%
22571
 
< 0.1%
22581
 
< 0.1%
22591
 
< 0.1%
22601
 
< 0.1%
Other values (3380)3380
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
33891
< 0.1%
33881
< 0.1%
33871
< 0.1%
33861
< 0.1%
33851
< 0.1%
33841
< 0.1%
33831
< 0.1%
33821
< 0.1%
33811
< 0.1%
33801
< 0.1%

age
Real number (ℝ)

Distinct39
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.542183
Minimum32
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.6 KiB
2025-12-27T00:11:33.201566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile37
Q142
median49
Q356
95-th percentile64
Maximum70
Range38
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.5928781
Coefficient of variation (CV)0.17344569
Kurtosis-1.0048019
Mean49.542183
Median Absolute Deviation (MAD)7
Skewness0.22579588
Sum167948
Variance73.837553
MonotonicityNot monotonic
2025-12-27T00:11:33.376631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
40148
 
4.4%
42145
 
4.3%
41144
 
4.2%
46140
 
4.1%
39139
 
4.1%
44135
 
4.0%
48134
 
4.0%
45131
 
3.9%
43127
 
3.7%
38119
 
3.5%
Other values (29)2028
59.8%
ValueCountFrequency (%)
321
 
< 0.1%
334
 
0.1%
3416
 
0.5%
3529
 
0.9%
3675
2.2%
3773
2.2%
38119
3.5%
39139
4.1%
40148
4.4%
41144
4.2%
ValueCountFrequency (%)
702
 
0.1%
695
 
0.1%
6814
 
0.4%
6733
 
1.0%
6630
 
0.9%
6543
1.3%
6475
2.2%
6393
2.7%
6280
2.4%
6187
2.6%

education
Categorical

Missing 

Distinct4
Distinct (%)0.1%
Missing87
Missing (%)2.6%
Memory size172.6 KiB
1.0
1391 
2.0
990 
3.0
549 
4.0
373 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9909
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row4.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.01391
41.0%
2.0990
29.2%
3.0549
 
16.2%
4.0373
 
11.0%
(Missing)87
 
2.6%

Length

2025-12-27T00:11:33.558587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-27T00:11:33.711895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01391
42.1%
2.0990
30.0%
3.0549
 
16.6%
4.0373
 
11.3%

Most occurring characters

ValueCountFrequency (%)
.3303
33.3%
03303
33.3%
11391
14.0%
2990
 
10.0%
3549
 
5.5%
4373
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)9909
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.3303
33.3%
03303
33.3%
11391
14.0%
2990
 
10.0%
3549
 
5.5%
4373
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)9909
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.3303
33.3%
03303
33.3%
11391
14.0%
2990
 
10.0%
3549
 
5.5%
4373
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)9909
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.3303
33.3%
03303
33.3%
11391
14.0%
2990
 
10.0%
3549
 
5.5%
4373
 
3.8%

sex
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size165.7 KiB
F
1923 
M
1467 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3390
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowM
3rd rowF
4th rowM
5th rowF

Common Values

ValueCountFrequency (%)
F1923
56.7%
M1467
43.3%

Length

2025-12-27T00:11:33.879757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-27T00:11:34.012915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
f1923
56.7%
m1467
43.3%

Most occurring characters

ValueCountFrequency (%)
F1923
56.7%
M1467
43.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)3390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F1923
56.7%
M1467
43.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F1923
56.7%
M1467
43.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F1923
56.7%
M1467
43.3%

is_smoking
Boolean

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
False
1703 
True
1687 
ValueCountFrequency (%)
False1703
50.2%
True1687
49.8%
2025-12-27T00:11:34.123811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

cigsPerDay
Real number (ℝ)

High correlation  Zeros 

Distinct32
Distinct (%)1.0%
Missing22
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean9.0694774
Minimum0
Maximum70
Zeros1703
Zeros (%)50.2%
Negative0
Negative (%)0.0%
Memory size26.6 KiB
2025-12-27T00:11:34.262573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320
95-th percentile30
Maximum70
Range70
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.879078
Coefficient of variation (CV)1.3097863
Kurtosis0.97552913
Mean9.0694774
Median Absolute Deviation (MAD)0
Skewness1.2230054
Sum30546
Variance141.11249
MonotonicityNot monotonic
2025-12-27T00:11:34.430278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
01703
50.2%
20606
 
17.9%
30176
 
5.2%
15172
 
5.1%
10106
 
3.1%
9104
 
3.1%
5103
 
3.0%
379
 
2.3%
4062
 
1.8%
148
 
1.4%
Other values (22)209
 
6.2%
ValueCountFrequency (%)
01703
50.2%
148
 
1.4%
217
 
0.5%
379
 
2.3%
47
 
0.2%
5103
 
3.0%
614
 
0.4%
78
 
0.2%
810
 
0.3%
9104
 
3.1%
ValueCountFrequency (%)
701
 
< 0.1%
608
 
0.2%
506
 
0.2%
452
 
0.1%
4342
 
1.2%
4062
 
1.8%
381
 
< 0.1%
3517
 
0.5%
30176
5.2%
2544
 
1.3%

BPMeds
Categorical

Imbalance  Missing 

Distinct2
Distinct (%)0.1%
Missing44
Missing (%)1.3%
Memory size172.4 KiB
0.0
3246 
1.0
 
100

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10038
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03246
95.8%
1.0100
 
2.9%
(Missing)44
 
1.3%

Length

2025-12-27T00:11:34.611925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-27T00:11:34.747494image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.03246
97.0%
1.0100
 
3.0%

Most occurring characters

ValueCountFrequency (%)
06592
65.7%
.3346
33.3%
1100
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)10038
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
06592
65.7%
.3346
33.3%
1100
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10038
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
06592
65.7%
.3346
33.3%
1100
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10038
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
06592
65.7%
.3346
33.3%
1100
 
1.0%

prevalentStroke
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size165.7 KiB
0
3368 
1
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3390
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03368
99.4%
122
 
0.6%

Length

2025-12-27T00:11:34.898081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-27T00:11:35.025245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
03368
99.4%
122
 
0.6%

Most occurring characters

ValueCountFrequency (%)
03368
99.4%
122
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)3390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
03368
99.4%
122
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
03368
99.4%
122
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
03368
99.4%
122
 
0.6%

prevalentHyp
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size165.7 KiB
0
2321 
1
1069 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3390
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
02321
68.5%
11069
31.5%

Length

2025-12-27T00:11:35.173179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-27T00:11:35.316303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
02321
68.5%
11069
31.5%

Most occurring characters

ValueCountFrequency (%)
02321
68.5%
11069
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)3390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02321
68.5%
11069
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02321
68.5%
11069
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02321
68.5%
11069
31.5%

diabetes
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size165.7 KiB
0
3303 
1
 
87

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3390
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03303
97.4%
187
 
2.6%

Length

2025-12-27T00:11:35.466143image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-27T00:11:35.605515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
03303
97.4%
187
 
2.6%

Most occurring characters

ValueCountFrequency (%)
03303
97.4%
187
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)3390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
03303
97.4%
187
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
03303
97.4%
187
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
03303
97.4%
187
 
2.6%

totChol
Real number (ℝ)

Missing 

Distinct240
Distinct (%)7.2%
Missing38
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean237.07428
Minimum107
Maximum696
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.6 KiB
2025-12-27T00:11:35.743112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum107
5-th percentile169
Q1206
median234
Q3264
95-th percentile313.45
Maximum696
Range589
Interquartile range (IQR)58

Descriptive statistics

Standard deviation45.24743
Coefficient of variation (CV)0.1908576
Kurtosis4.7813217
Mean237.07428
Median Absolute Deviation (MAD)29
Skewness0.9406357
Sum794673
Variance2047.3299
MonotonicityNot monotonic
2025-12-27T00:11:35.917034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24065
 
1.9%
21051
 
1.5%
22048
 
1.4%
26046
 
1.4%
23245
 
1.3%
23043
 
1.3%
22542
 
1.2%
25041
 
1.2%
20041
 
1.2%
27040
 
1.2%
Other values (230)2890
85.3%
ValueCountFrequency (%)
1071
< 0.1%
1131
< 0.1%
1191
< 0.1%
1241
< 0.1%
1261
< 0.1%
1291
< 0.1%
1331
< 0.1%
1352
0.1%
1371
< 0.1%
1401
< 0.1%
ValueCountFrequency (%)
6961
 
< 0.1%
6001
 
< 0.1%
4641
 
< 0.1%
4531
 
< 0.1%
4391
 
< 0.1%
4321
 
< 0.1%
4103
0.1%
3981
 
< 0.1%
3921
 
< 0.1%
3912
0.1%

sysBP
Real number (ℝ)

High correlation 

Distinct226
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.60118
Minimum83.5
Maximum295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.6 KiB
2025-12-27T00:11:36.100438image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum83.5
5-th percentile104
Q1117
median128.5
Q3144
95-th percentile175.275
Maximum295
Range211.5
Interquartile range (IQR)27

Descriptive statistics

Standard deviation22.29203
Coefficient of variation (CV)0.16811336
Kurtosis2.3659225
Mean132.60118
Median Absolute Deviation (MAD)13.5
Skewness1.1758367
Sum449518
Variance496.93458
MonotonicityNot monotonic
2025-12-27T00:11:36.282974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11087
 
2.6%
12085
 
2.5%
13085
 
2.5%
12569
 
2.0%
11568
 
2.0%
12463
 
1.9%
13561
 
1.8%
12861
 
1.8%
12659
 
1.7%
12258
 
1.7%
Other values (216)2694
79.5%
ValueCountFrequency (%)
83.52
 
0.1%
851
 
< 0.1%
85.51
 
< 0.1%
902
 
0.1%
92.51
 
< 0.1%
932
 
0.1%
93.52
 
0.1%
942
 
0.1%
955
0.1%
95.51
 
< 0.1%
ValueCountFrequency (%)
2951
 
< 0.1%
2481
 
< 0.1%
2441
 
< 0.1%
2431
 
< 0.1%
2351
 
< 0.1%
2321
 
< 0.1%
2301
 
< 0.1%
2202
0.1%
2171
 
< 0.1%
2153
0.1%

diaBP
Real number (ℝ)

High correlation 

Distinct142
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.883038
Minimum48
Maximum142.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.6 KiB
2025-12-27T00:11:36.463073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile66
Q174.5
median82
Q390
95-th percentile105
Maximum142.5
Range94.5
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation12.023581
Coefficient of variation (CV)0.14506685
Kurtosis1.273995
Mean82.883038
Median Absolute Deviation (MAD)8
Skewness0.71817267
Sum280973.5
Variance144.5665
MonotonicityNot monotonic
2025-12-27T00:11:36.646819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80213
 
6.3%
82123
 
3.6%
70109
 
3.2%
85107
 
3.2%
90100
 
2.9%
8796
 
2.8%
8194
 
2.8%
8494
 
2.8%
7590
 
2.7%
7889
 
2.6%
Other values (132)2275
67.1%
ValueCountFrequency (%)
481
 
< 0.1%
501
 
< 0.1%
511
 
< 0.1%
522
 
0.1%
531
 
< 0.1%
541
 
< 0.1%
552
 
0.1%
562
 
0.1%
575
0.1%
57.52
 
0.1%
ValueCountFrequency (%)
142.51
 
< 0.1%
1362
 
0.1%
1352
 
0.1%
1332
 
0.1%
1305
0.1%
1291
 
< 0.1%
1281
 
< 0.1%
127.51
 
< 0.1%
1253
0.1%
124.51
 
< 0.1%

BMI
Real number (ℝ)

Distinct1259
Distinct (%)37.3%
Missing14
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean25.794964
Minimum15.96
Maximum56.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.6 KiB
2025-12-27T00:11:36.825002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum15.96
5-th percentile20.06
Q123.02
median25.38
Q328.04
95-th percentile32.8525
Maximum56.8
Range40.84
Interquartile range (IQR)5.02

Descriptive statistics

Standard deviation4.1154488
Coefficient of variation (CV)0.15954466
Kurtosis2.8346067
Mean25.794964
Median Absolute Deviation (MAD)2.49
Skewness1.022252
Sum87083.8
Variance16.936918
MonotonicityNot monotonic
2025-12-27T00:11:37.019072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.9117
 
0.5%
22.5416
 
0.5%
22.1915
 
0.4%
23.4812
 
0.4%
25.0912
 
0.4%
23.111
 
0.3%
24.5611
 
0.3%
23.0911
 
0.3%
22.7311
 
0.3%
25.9411
 
0.3%
Other values (1249)3249
95.8%
(Missing)14
 
0.4%
ValueCountFrequency (%)
15.961
< 0.1%
16.481
< 0.1%
16.591
< 0.1%
16.611
< 0.1%
16.691
< 0.1%
16.711
< 0.1%
16.731
< 0.1%
16.751
< 0.1%
16.921
< 0.1%
16.981
< 0.1%
ValueCountFrequency (%)
56.81
< 0.1%
51.281
< 0.1%
45.81
< 0.1%
45.791
< 0.1%
44.711
< 0.1%
44.551
< 0.1%
44.271
< 0.1%
44.091
< 0.1%
43.691
< 0.1%
43.671
< 0.1%

heartRate
Real number (ℝ)

Distinct68
Distinct (%)2.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean75.977279
Minimum45
Maximum143
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.6 KiB
2025-12-27T00:11:37.205450image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile60
Q168
median75
Q383
95-th percentile98
Maximum143
Range98
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.971868
Coefficient of variation (CV)0.15757169
Kurtosis0.9796436
Mean75.977279
Median Absolute Deviation (MAD)7
Skewness0.67648972
Sum257487
Variance143.32563
MonotonicityNot monotonic
2025-12-27T00:11:37.391523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75442
 
13.0%
80298
 
8.8%
70241
 
7.1%
85191
 
5.6%
72184
 
5.4%
60183
 
5.4%
65152
 
4.5%
90143
 
4.2%
68133
 
3.9%
6376
 
2.2%
Other values (58)1346
39.7%
ValueCountFrequency (%)
451
 
< 0.1%
471
 
< 0.1%
484
 
0.1%
5015
0.4%
511
 
< 0.1%
5212
0.4%
538
 
0.2%
5410
 
0.3%
5525
0.7%
5617
0.5%
ValueCountFrequency (%)
1431
 
< 0.1%
1401
 
< 0.1%
1253
 
0.1%
1222
 
0.1%
1205
 
0.1%
1154
 
0.1%
1123
 
0.1%
11033
1.0%
1087
 
0.2%
1072
 
0.1%

glucose
Real number (ℝ)

High correlation  Missing 

Distinct132
Distinct (%)4.3%
Missing304
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean82.08652
Minimum40
Maximum394
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.6 KiB
2025-12-27T00:11:37.559497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile62
Q171
median78
Q387
95-th percentile110
Maximum394
Range354
Interquartile range (IQR)16

Descriptive statistics

Standard deviation24.244753
Coefficient of variation (CV)0.29535609
Kurtosis57.356963
Mean82.08652
Median Absolute Deviation (MAD)8
Skewness6.1443897
Sum253319
Variance587.80807
MonotonicityNot monotonic
2025-12-27T00:11:37.754051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75149
 
4.4%
83135
 
4.0%
70123
 
3.6%
77122
 
3.6%
80118
 
3.5%
78117
 
3.5%
73116
 
3.4%
74114
 
3.4%
85112
 
3.3%
76104
 
3.1%
Other values (122)1876
55.3%
(Missing)304
 
9.0%
ValueCountFrequency (%)
401
 
< 0.1%
431
 
< 0.1%
442
 
0.1%
453
0.1%
472
 
0.1%
481
 
< 0.1%
502
 
0.1%
522
 
0.1%
535
0.1%
543
0.1%
ValueCountFrequency (%)
3942
0.1%
3861
< 0.1%
3681
< 0.1%
3481
< 0.1%
3321
< 0.1%
3201
< 0.1%
2971
< 0.1%
2941
< 0.1%
2741
< 0.1%
2701
< 0.1%

TenYearCHD
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size165.7 KiB
0
2879 
1
511 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3390
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
02879
84.9%
1511
 
15.1%

Length

2025-12-27T00:11:38.117605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-27T00:11:38.244654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
02879
84.9%
1511
 
15.1%

Most occurring characters

ValueCountFrequency (%)
02879
84.9%
1511
 
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)3390
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
02879
84.9%
1511
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3390
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
02879
84.9%
1511
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3390
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
02879
84.9%
1511
 
15.1%

Interactions

2025-12-27T00:11:30.641448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:21.219622image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:22.537270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:23.679959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:24.887631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:26.019465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:27.126515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:28.347731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:29.559927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:30.771477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:21.560310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:22.664629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:23.807771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:25.036402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:26.143252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:27.250394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:28.495545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:29.687953image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:30.899677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:21.682632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:22.797926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:24.030794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:25.157153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:26.274511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:27.387184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:28.637846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:29.808709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:31.035041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:21.821971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:22.924692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:24.152035image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:25.297969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:26.405992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:27.516495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:28.775136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:29.930161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:31.163532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:21.947698image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:23.049007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:24.284831image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:25.412694image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:26.527212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:27.744944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:28.907912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:30.051949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:31.280874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:22.053212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:23.172829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:24.390023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:25.528453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:26.636390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:27.854129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:29.045218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:30.166380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:31.397835image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:22.168951image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:23.302658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:24.513800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:25.633651image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:26.747575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:27.971422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:29.170976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:30.276756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:31.536713image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:22.295251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:23.444973image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:24.632062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:25.763874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:26.875884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:28.110728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:29.303056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:30.409413image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:31.654088image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:22.414534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:23.551733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:24.745817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:25.881670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:26.998681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:28.218985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:29.429384image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-12-27T00:11:30.519997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-12-27T00:11:38.354403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
BMIBPMedsTenYearCHDagecigsPerDaydiaBPdiabeteseducationglucoseheartRateidis_smokingprevalentHypprevalentStrokesexsysBPtotChol
BMI1.0000.1210.0780.147-0.1450.3790.1060.0860.0730.0630.0400.1640.2930.2230.2090.3320.153
BPMeds0.1211.0000.0840.1340.0000.2250.0630.0000.1140.0910.0150.0320.2570.1070.0390.3010.078
TenYearCHD0.0780.0841.0000.2190.0580.1470.1000.0760.1380.0000.0000.0290.1650.0610.0820.2100.088
age0.1470.1340.2191.000-0.2120.2200.1000.1480.111-0.0050.0150.2220.3010.0500.0180.4040.303
cigsPerDay-0.1450.0000.058-0.2121.000-0.1040.0290.042-0.0970.076-0.0080.8460.1150.0000.341-0.129-0.038
diaBP0.3790.2250.1470.220-0.1041.0000.0530.0500.0510.173-0.0040.1220.6390.0470.0740.7730.180
diabetes0.1060.0630.1000.1000.0290.0531.0000.0570.7400.0340.0000.0490.0790.0000.0000.1380.107
education0.0860.0000.0760.1480.0420.0500.0571.0000.0300.0300.0330.0620.0880.0190.1390.0780.018
glucose0.0730.1140.1380.111-0.0970.0510.7400.0301.0000.0950.0240.0790.0730.0410.0270.1160.036
heartRate0.0630.0910.000-0.0050.0760.1730.0340.0300.0951.0000.0190.0700.1500.0000.1210.1660.082
id0.0400.0150.0000.015-0.008-0.0040.0000.0330.0240.0191.0000.0180.0410.0160.0000.019-0.014
is_smoking0.1640.0320.0290.2220.8460.1220.0490.0620.0790.0700.0181.0000.1170.0360.2140.1370.052
prevalentHyp0.2930.2570.1650.3010.1150.6390.0790.0880.0730.1500.0410.1171.0000.0650.0000.7150.154
prevalentStroke0.2230.1070.0610.0500.0000.0470.0000.0190.0410.0000.0160.0360.0651.0000.0000.0490.000
sex0.2090.0390.0820.0180.3410.0740.0000.1390.0270.1210.0000.2140.0000.0001.0000.1030.077
sysBP0.3320.3010.2100.404-0.1290.7730.1380.0780.1160.1660.0190.1370.7150.0490.1031.0000.220
totChol0.1530.0780.0880.303-0.0380.1800.1070.0180.0360.082-0.0140.0520.1540.0000.0770.2201.000

Missing values

2025-12-27T00:11:31.848632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-27T00:11:32.170587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-12-27T00:11:32.580537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idageeducationsexis_smokingcigsPerDayBPMedsprevalentStrokeprevalentHypdiabetestotCholsysBPdiaBPBMIheartRateglucoseTenYearCHD
00642.0FYES3.00.0000221.0148.085.0NaN90.080.01
11364.0MNO0.00.0010212.0168.098.029.7772.075.00
22461.0FYES10.00.0000250.0116.071.020.3588.094.00
33501.0MYES20.00.0010233.0158.088.028.2668.094.01
44641.0FYES30.00.0000241.0136.585.026.4270.077.00
55613.0FNO0.00.0010272.0182.0121.032.8085.065.01
66611.0MNO0.00.0010238.0232.0136.024.8375.079.00
77364.0MYES35.00.0000295.0102.068.028.1560.063.00
88412.0FYES20.0NaN000220.0126.078.020.7086.079.00
99552.0FNO0.00.0010326.0144.081.025.7185.0NaN0
idageeducationsexis_smokingcigsPerDayBPMedsprevalentStrokeprevalentHypdiabetestotCholsysBPdiaBPBMIheartRateglucoseTenYearCHD
33803380561.0FYES20.00.0000240.0125.079.027.3880.082.00
33813381631.0FNO0.00.0000205.0138.071.033.1160.085.01
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